Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT

TL;DR

This paper introduces TempPNet, an interpretable deep learning model that detects depression from motion sensor data, capturing temporal progressions and providing visual explanations to support collaborative care in chronic disease management.

Contribution

It presents a novel interpretable deep learning approach, TempPNet, capable of modeling temporal patterns in sensor data for depression detection, enhancing transparency and trust in health sensing applications.

Findings

TempPNet outperforms existing benchmarks in depression detection accuracy.

The model provides visual interpretability of depression progression and symptoms.

User and expert studies confirm TempPNet's superior interpretability.

Abstract

Health sensing for chronic disease management creates immense benefits for social welfare. Existing health sensing studies primarily focus on the prediction of physical chronic diseases. Depression, a widespread complication of chronic diseases, is however understudied. We draw on the medical literature to support depression detection using motion sensor data. To connect humans in this decision-making, safeguard trust, and ensure algorithm transparency, we develop an interpretable deep learning model: Temporal Prototype Network (TempPNet). TempPNet is built upon the emergent prototype learning models. To accommodate the temporal characteristic of sensor data and the progressive property of depression, TempPNet differs from existing prototype learning models in its capability of capturing temporal progressions of prototypes. Extensive empirical analyses using real-world motion sensor data show that TempPNet outperforms state-of-the-art benchmarks in depression detection. Moreover, TempPNet interprets its decision by visualizing the temporal progression of depression and its corresponding symptoms detected from sensor data. We further employ a user study and a medical expert panel to demonstrate its superiority over the benchmarks in interpretability. This study offers an algorithmic solution for impactful social good -- collaborative care of chronic diseases and depression in health sensing. Methodologically, it contributes to extant literature with a novel interpretable deep learning model for depression detection from sensor data. Patients, doctors, and caregivers can deploy our model on mobile devices to monitor patients' depression risks in real-time. Our model's interpretability also allows human experts to participate in the decision-making by reviewing the interpretation and making informed interventions.